论文标题
徒手产科超声的自动探测运动指导
Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound
论文作者
论文摘要
我们提出了第一个提供实时探测运动指南的系统，以在常规徒手产科超声扫描中获取标准平面。这样的系统可以通过降低所需的运营商专业水平来促进全球产科超声扫描的部署。该系统采用人工神经网络，该神经网络接收超声视频信号和附着在探测器上的惯性测量单元（IMU）的运动信号，并预测指导信号。该网络称为US-GUIDENET可以预测向标准平面位置（目标预测）的运动，或者专家超声师将执行的下一个运动（行动预测）。尽管其他超声应用程序的现有模型是通过模拟或幻影培训的，但我们通过17名认可的超声波检查员从464例常规临床扫描中使用现实世界中的超声视频和探测运动数据来训练模型。对3种标准平面类型的评估表明，该模型提供了一个有用的指南信号，目标预测的精度为88.8％，而动作预测则提供了90.9％。
We present the first system that provides real-time probe movement guidance for acquiring standard planes in routine freehand obstetric ultrasound scanning. Such a system can contribute to the worldwide deployment of obstetric ultrasound scanning by lowering the required level of operator expertise. The system employs an artificial neural network that receives the ultrasound video signal and the motion signal of an inertial measurement unit (IMU) that is attached to the probe, and predicts a guidance signal. The network termed US-GuideNet predicts either the movement towards the standard plane position (goal prediction), or the next movement that an expert sonographer would perform (action prediction). While existing models for other ultrasound applications are trained with simulations or phantoms, we train our model with real-world ultrasound video and probe motion data from 464 routine clinical scans by 17 accredited sonographers. Evaluations for 3 standard plane types show that the model provides a useful guidance signal with an accuracy of 88.8% for goal prediction and 90.9% for action prediction.
